# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------

import torch
import torch.nn as nn
import numpy as np
import math
from timm.models.vision_transformer import PatchEmbed, Attention, Mlp


def modulate(x, shift, scale):
	return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)


#################################################################################
#			   Embedding Layers for Timesteps and Class Labels				 #
#################################################################################

class TimestepEmbedder(nn.Module):
	"""
	Embeds scalar timesteps into vector representations.
	"""
	def __init__(self, hidden_size, frequency_embedding_size=256):
		super().__init__()
		self.mlp = nn.Sequential(
			nn.Linear(frequency_embedding_size, hidden_size, bias=True),
			nn.SiLU(),
			nn.Linear(hidden_size, hidden_size, bias=True),
		)
		self.frequency_embedding_size = frequency_embedding_size

	@staticmethod
	def timestep_embedding(t, dim, max_period=10000):
		"""
		Create sinusoidal timestep embeddings.
		:param t: a 1-D Tensor of N indices, one per batch element.
						  These may be fractional.
		:param dim: the dimension of the output.
		:param max_period: controls the minimum frequency of the embeddings.
		:return: an (N, D) Tensor of positional embeddings.
		"""
		# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
		half = dim // 2
		freqs = torch.exp(
			-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
		).to(device=t.device)
		args = t[:, None].float() * freqs[None]
		embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
		if dim % 2:
			embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
		return embedding.to(dtype=t.dtype)

	def forward(self, t):
		t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
		t_emb = self.mlp(t_freq)
		return t_emb


class LabelEmbedder(nn.Module):
	"""
	Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
	"""
	def __init__(self, num_classes, hidden_size, dropout_prob):
		super().__init__()
		use_cfg_embedding = dropout_prob > 0
		self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
		self.num_classes = num_classes
		self.dropout_prob = dropout_prob

	def token_drop(self, labels, force_drop_ids=None):
		"""
		Drops labels to enable classifier-free guidance.
		"""
		if force_drop_ids is None:
			drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
		else:
			drop_ids = force_drop_ids == 1
		labels = torch.where(drop_ids, self.num_classes, labels)
		return labels

	def forward(self, labels, train, force_drop_ids=None):
		use_dropout = self.dropout_prob > 0
		if (train and use_dropout) or (force_drop_ids is not None):
			labels = self.token_drop(labels, force_drop_ids)
		embeddings = self.embedding_table(labels)
		return embeddings


#################################################################################
#								 Core DiT Model								#
#################################################################################

class DiTBlock(nn.Module):
	"""
	A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
	"""
	def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
		super().__init__()
		self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
		self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
		self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
		mlp_hidden_dim = int(hidden_size * mlp_ratio)
		approx_gelu = lambda: nn.GELU(approximate="tanh")
		self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
		self.adaLN_modulation = nn.Sequential(
			nn.SiLU(),
			nn.Linear(hidden_size, 6 * hidden_size, bias=True)
		)

	def forward(self, x, c):
		shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
		x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa))
		x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
		return x


class FinalLayer(nn.Module):
	"""
	The final layer of DiT.
	"""
	def __init__(self, hidden_size, patch_size, out_channels):
		super().__init__()
		self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
		self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
		self.adaLN_modulation = nn.Sequential(
			nn.SiLU(),
			nn.Linear(hidden_size, 2 * hidden_size, bias=True)
		)

	def forward(self, x, c):
		shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
		x = modulate(self.norm_final(x), shift, scale)
		x = self.linear(x)
		return x


class DiT(nn.Module):
	"""
	Diffusion model with a Transformer backbone.
	"""
	def __init__(
		self,
		input_size=32,
		patch_size=2,
		in_channels=4,
		hidden_size=1152,
		depth=28,
		num_heads=16,
		mlp_ratio=4.0,
		class_dropout_prob=0.1,
		num_classes=1000,
		learn_sigma=True,
		**kwargs,
	):
		super().__init__()
		self.learn_sigma = learn_sigma
		self.in_channels = in_channels
		self.out_channels = in_channels * 2 if learn_sigma else in_channels
		self.patch_size = patch_size
		self.num_heads = num_heads

		self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
		self.t_embedder = TimestepEmbedder(hidden_size)
		self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
		num_patches = self.x_embedder.num_patches
		# Will use fixed sin-cos embedding:
		self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)

		self.blocks = nn.ModuleList([
			DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)
		])
		self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
		self.initialize_weights()

	def initialize_weights(self):
		# Initialize transformer layers:
		def _basic_init(module):
			if isinstance(module, nn.Linear):
				torch.nn.init.xavier_uniform_(module.weight)
				if module.bias is not None:
					nn.init.constant_(module.bias, 0)
		self.apply(_basic_init)

		# Initialize (and freeze) pos_embed by sin-cos embedding:
		pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
		self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

		# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
		w = self.x_embedder.proj.weight.data
		nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
		nn.init.constant_(self.x_embedder.proj.bias, 0)

		# Initialize label embedding table:
		nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)

		# Initialize timestep embedding MLP:
		nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
		nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

		# Zero-out adaLN modulation layers in DiT blocks:
		for block in self.blocks:
			nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
			nn.init.constant_(block.adaLN_modulation[-1].bias, 0)

		# Zero-out output layers:
		nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
		nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
		nn.init.constant_(self.final_layer.linear.weight, 0)
		nn.init.constant_(self.final_layer.linear.bias, 0)

	def unpatchify(self, x):
		"""
		x: (N, T, patch_size**2 * C)
		imgs: (N, H, W, C)
		"""
		c = self.out_channels
		p = self.x_embedder.patch_size[0]
		h = w = int(x.shape[1] ** 0.5)
		assert h * w == x.shape[1]

		x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
		x = torch.einsum('nhwpqc->nchpwq', x)
		imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
		return imgs

	def forward_raw(self, x, t, y):
		"""
		Forward pass of DiT.
		x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
		t: (N,) tensor of diffusion timesteps
		y: (N,) tensor of class labels
		"""
		x = self.x_embedder(x) + self.pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
		t = self.t_embedder(t)                   # (N, D)
		y = self.y_embedder(y, self.training)    # (N, D)
		c = t + y                                # (N, D)
		for block in self.blocks:
			x = block(x, c)                      # (N, T, D)
		x = self.final_layer(x, c)                # (N, T, patch_size ** 2 * out_channels)
		x = self.unpatchify(x)                   # (N, out_channels, H, W)
		return x

	def forward(self, x, timesteps, context, y=None, **kwargs):
		"""
		Forward pass that adapts comfy input to original forward function
		x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
		timesteps: (N,) tensor of diffusion timesteps
		context: (N, [LabelID]) conditioning
		y: extra conditioning.
		"""
		## Remove outer array from cond
		context = context[:, 0]

		## run original forward pass
		out = self.forward_raw(
			x = x.to(self.dtype),
			t = timesteps.to(self.dtype),
			y = context.to(torch.int),
		)

		## only return EPS
		out = out.to(torch.float)
		eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
		return eps

#################################################################################
#				   Sine/Cosine Positional Embedding Functions				  #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py

def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
	"""
	grid_size: int of the grid height and width
	return:
	pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
	"""
	grid_h = np.arange(grid_size, dtype=np.float32)
	grid_w = np.arange(grid_size, dtype=np.float32)
	grid = np.meshgrid(grid_w, grid_h)  # here w goes first
	grid = np.stack(grid, axis=0)

	grid = grid.reshape([2, 1, grid_size, grid_size])
	pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
	if cls_token and extra_tokens > 0:
		pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
	return pos_embed


def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
	assert embed_dim % 2 == 0

	# use half of dimensions to encode grid_h
	emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
	emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

	emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
	return emb


def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
	"""
	embed_dim: output dimension for each position
	pos: a list of positions to be encoded: size (M,)
	out: (M, D)
	"""
	assert embed_dim % 2 == 0
	omega = np.arange(embed_dim // 2, dtype=np.float64)
	omega /= embed_dim / 2.
	omega = 1. / 10000**omega  # (D/2,)

	pos = pos.reshape(-1)  # (M,)
	out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product

	emb_sin = np.sin(out) # (M, D/2)
	emb_cos = np.cos(out) # (M, D/2)

	emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
	return emb
